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Fake News Detection using NLP and Deep Learning
Subject area: Science,Engineering and Technology · Area of research: NLP and Deep Learning
DOI: https://doi.org/10.64388/IREV9I11-1717820
Abstract
The continuous growth of digital communication platforms has made information sharing easier and faster than ever before. At the same time, the spread of false and misleading news through social media, blogs, and online news portals has become a major global concern. Fake news can influence public opinion, create social panic, and negatively affect political, economic, and healthcare systems. To address this issue, researchers have focused on developing intelligent systems capable of automatically identifying fake information. This research paper presents a detailed study of fake news detection using Natural Language Processing (NLP) and Deep Learning techniques. The proposed approach applies multiple NLP preprocessing methods such as tokenization, stop-word elimination, stemming, lemmatization, and text vectorization to prepare news articles for analysis. Different deep learning models including Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), and transformer-based BERT models are used for classification. The study also compares traditional machine learning algorithms with modern deep learning techniques. Experimental observations show that transformer-based architectures provide better contextual understanding and higher prediction accuracy. The proposed system aims to support automated misinformation detection and improve the reliability of online content.
Keywords
Fake News Detection, NLP, Deep Learning, CNN, LSTM, BERT, Machine Learning, Artificial Intelligence.
How to cite this paper
@article{1717820,
author = {Pradip Subhash Ahire, Niraj Mahendra Lokhande, Prof. Gadekar M. J.},
title = {Fake News Detection using NLP and Deep Learning},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {9},
number = {11},
pages = {4366-4370},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1717820.pdf},
abstract = {The continuous growth of digital communication platforms has made information sharing easier and faster than ever before. At the same time, the spread of false and misleading news through social media, blogs, and online news portals has become a major global concern. Fake news can influence public opinion, create social panic, and negatively affect political, economic, and healthcare systems. To address this issue, researchers have focused on developing intelligent systems capable of automatically identifying fake information. This research paper presents a detailed study of fake news detection using Natural Language Processing (NLP) and Deep Learning techniques. The proposed approach applies multiple NLP preprocessing methods such as tokenization, stop-word elimination, stemming, lemmatization, and text vectorization to prepare news articles for analysis. Different deep learning models including Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), and transformer-based BERT models are used for classification. The study also compares traditional machine learning algorithms with modern deep learning techniques. Experimental observations show that transformer-based architectures provide better contextual understanding and higher prediction accuracy. The proposed system aims to support automated misinformation detection and improve the reliability of online content.},
keywords = {Fake News Detection, NLP, Deep Learning, CNN, LSTM, BERT, Machine Learning, Artificial Intelligence.},
month = {May},
doi = {https://doi.org/10.64388/IREV9I11-1717820}
}